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4. Models Deployment and Consumption (20-25%):
- Designer pipeline publishing as a web service: The candidates should show their knowledge of target compute resources creation, inference pipelines configuration, and deployed endpoints consuming.
- Production computes targets creation: The test takers should perform their skills in compute options evaluation for deployment and consideration of security for deployed services.
- Model-as-a-Service deployment: This subtopic will measure the individuals’ expertise in configuring deployment settings, troubleshooting issues with deployment containers, and consuming deployed services.
- Creation of pipelines for batch inferencing: This subject area covers your competence in running batch inferencing pipelines and obtaining outputs as well as publishing batch inferencing pipelines.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
How to Register For Exam DP-100: Designing and Implementing a Data Science Solution on Azure?
Microsoft DP-100日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Explore data and train models | 35-40% | - Run experiments and train models
|
| Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
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